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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

1. Introduction to Meta-heuristic Optimization Algorithms

verfasst von : Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier

Erschienen in: Modern Music-Inspired Optimization Algorithms for Electric Power Systems

Verlag: Springer International Publishing

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Abstract

This chapter begins with a concise definition of the optimization problem and its parameters, along with a mathematical description of an optimization problem with continuous and discrete decision-making variables whose objective functions are employed in a standard form of an optimization problem along with equality and inequality constraints. Subsequently, the authors address the classifications of an optimization problem from different perspectives, which deserve attention and can achieve full knowledge regarding an optimization problem and its parameters. In addition, a succinct overview pertaining to the optimization algorithms with a focus on meta-heuristic optimization algorithms is reported.

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Nächstes Kapitel Introduction to Multi-objective Optimization and Decision-Making Analysis
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Literatur
1.
S.S. Rao, Engineering Optimization, Theory and Practice, 4th edn. (Wiley, New York, 2009)CrossRef
2.
F. Rothlauf, Design of Modern Heuristics: Principles and Application (Springer, New York, 2011)CrossRefMATH
3.
K.K.H. Ng, C.K.M. Lee, F.T.S. Chan, Y. Lv, Review on meta-heuristics approaches for airside operation research. Appl. Soft Comput. 66, 104–133 (2018)CrossRef
4.
5.
I. Fister Jr., X.S. Yang, I. Fister, J. Brest, D. Fister, A brief review of nature-inspired algorithms for optimization. Elektrotehniški Vestnik 80(3), 1–7 (2013)MATH
6.
J. Kennedy, R. Eberhart, Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)
7.
M. Dorigo, Optimization, learning and natural algorithms. Ph.D. Dissertation, Politecnico di Milano, Italy, 1992
8.
X.S. Yang, Firefly algorithm, stochastic test functions and design optimization. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)CrossRef
9.
X.S. Yang, A new metaheuristic bat-inspired algorithm, in Nature Inspired Cooperative Strategies for Optimization, pp. 65–74, 2010
10.
K.M. Passino, Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control. Syst. 22(3), 52–67 (2002)MathSciNetCrossRef
11.
D. Teodorovic, M. Dell’orco, Bee colony optimization–a cooperative learning approach to complex transportation problems, in 16th Mini-EURO Conference on Advanced OR and AI Methods in Transportation, pp. 51–60, 2005
12.
R. Tang, S. Fong, X.S. Yang, S. Deb, Wolf search algorithm with ephemeral memory, in 7th International Conference on Digital Information Management, pp. 165–172, 2012
13.
X.S. Yang, S. Deb, Cuckoo search via Lévy flights, in World Congress on Nature & Biologically Inspired Computing, pp. 210–214, 2009
14.
R.S. Parpinelli, H.S. Lopes, New inspirations in swarm intelligence: a survey. Int. J. Bio-Inspired Comput. 3(1), 1–16 (2011)CrossRef
15.
J.H. Holland, Genetic Algorithms and Adaptation, in Adaptive Control of Ill-Defined Systems, NATO Conference Series (II Systems Science), vol. 16, (Springer, Boston, MA, 1984), pp. 317–333
16.
Y. Shi, An optimization algorithm based on brainstorming process. Int. J. Swarm Intell. Res. 2(4), 35–62 (2011)CrossRef
17.
A. Kaveh, N. Farhoudi, A new optimization method: dolphin echolocation. Adv. Eng. Softw. 59, 53–70 (2013)CrossRef
18.
M.M. Eusuff, K.E. Lansey, Optimization of water distribution network design using the shuffled frog leaping algorithm. J. Water Resour. Plan. Manag. 129(3), 210–225 (2003)CrossRef
19.
X.S. Yang, Flower pollination algorithm for global optimization, in International Conference on Unconventional Computing and Natural Computation, pp. 240–249, 2012
20.
A.K. Kar, Bio inspired computing–a review of algorithms and scope of applications. Expert Syst. Appl. 59, 20–32 (2016)CrossRef
21.
Z.W. Geem, J.H. Kim, G.V. Loganathan, A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRef
22.
E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)CrossRefMATH
23.
24.
E. Cuevas, D. Oliva, D. Zaldivar, M. Perez-Cisneros, H. Sossa, Circle detection using electromagnetism optimization. Inf. Sci. 182(1), 40–55 (2012)CrossRef
25.
Z. Zandi, E. Afjei, M. Sedighizadeh, Reactive power dispatch using big bang-big crunch optimization algorithm for voltage stability enhancement, in International Conference on Power and Energy, pp. 239–244, 2012
26.
A. Biswas, K.K. Mishra, S. Tiwari, A.K. Misra, Physics-inspired optimization algorithms: a survey. J. Opt. 2013, 1–15 (2013)
27.
N. Siddique, H. Adeli, Nature-inspired chemical reaction optimisation algorithms. Cogn. Comput. 9(4), 411–422 (2017)CrossRef
28.
E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, in IEEE Congress on Evolutionary Computation, pp. 4661–4667, 2007
29.
H. Shayeghi, J. Dadashpour, Anarchic society optimization based PID control of an automatic voltage regulator (AVR) system. Electr. Electron. Eng. 2(4), 199–207 (2012)CrossRef
30.
Y. Xu, Z. Cui, J. Zeng, Social emotional optimization algorithm for nonlinear constrained optimization problems, in International Conference on Swarm, Evolutionary, and Memetic Computing, pp. 583–590, 2010
31.
A. Husseinzadeh Kashan, League championship algorithm: a new algorithm for numerical function optimization, in International Conference of Soft Computing and Pattern Recognition, pp. 43–48, 2009
32.
P. Civicioglu, Artificial cooperative search algorithm for numerical optimization problems. Inf. Sci. 229, 58–76 (2013)CrossRefMATH
Metadaten
Titel
Introduction to Meta-heuristic Optimization Algorithms
verfasst von
Mohammad Kiani-Moghaddam
Mojtaba Shivaie
Philip D. Weinsier
Copyright-Jahr
2019
Buch
Modern Music-Inspired Optimization Algorithms for Electric Power Systems

Print ISBN: 978-3-030-12043-6

Electronic ISBN: 978-3-030-12044-3

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-12044-3_1